Shifts in functional traits and interactions patterns of soil methane-cycling communities following forest-to-pasture conversion in the Amazon Basin.

Deforestation threatens the integrity of the Amazon biome and the ecosystem services it provides, including greenhouse gas mitigation. Forest-to-pasture conversion has been shown to alter the flux of methane gas (CH4 ) in Amazonian soils, driving a switch from acting as a sink to a source of atmospheric CH4 . This study aimed to better understand this phenomenon by investigating soil microbial metagenomes, focusing on the taxonomic and functional structure of methane-cycling communities. Metagenomic data from forest and pasture soils were combined with measurements of in situ CH4 fluxes and soil edaphic factors and analysed using multivariate statistical approaches. We found a significantly higher abundance and diversity of methanogens in pasture soils. As inferred by co-occurrence networks, these microorganisms seem to be less interconnected within the soil microbiota in pasture soils. Metabolic traits were also different between land uses, with increased hydrogenotrophic and methylotrophic pathways of methanogenesis in pasture soils. Land-use change also induced shifts in taxonomic and functional traits of methanotrophs, with bacteria harbouring genes encoding the soluble form of methane monooxygenase enzyme (sMMO) depleted in pasture soils. Redundancy analysis and multimodel inference revealed that the shift in methane-cycling communities was associated with high pH, organic matter, soil porosity and micronutrients in pasture soils. These results comprehensively characterize the effect of forest-to-pasture conversion on the microbial communities driving the methane-cycling microorganisms in the Amazon rainforest, which will contribute to the efforts to preserve this important biome.

Increased soil moisture intensifies the impacts of forest-to-pasture conversion on methane emissions and methane-cycling communities in the Eastern Amazon.

Climatic changes are altering precipitation patterns in the Amazon and may influence soil methane (CH4) fluxes due to the differential responses of methanogenic and methanotrophic microorganisms. However, it remains unclear if these climate feedbacks can amplify land-use-related impacts on the CH4 cycle. To better predict the responses of soil CH4-cycling microorganisms and emissions under altered moisture levels in the Eastern Brazilian Amazon, we performed a 30-day microcosm experiment manipulating the moisture content (original moisture; 60%, 80%, and 100% of field capacity - FC) of forest and pasture soils. Gas samples were collected periodically for gas chromatography analysis, and methanogenic archaeal and methanotrophic bacterial communities were assessed using quantitative PCR and metagenomics. Positive and negative daily CH4 fluxes were observed for forest and pasture, indicating that these soils can act as both CH4 sources and sinks. Cumulative emissions and the abundance of methanogenesis-related genes and taxonomic groups were affected by land use, moisture, and their interaction. Pasture soils at 100% FC had the highest abundance of methanogens and CH4 emissions, 22 times higher than forest soils under the same treatment. Higher ratios of methanogens to methanotrophs were found in pasture than in forest soils, even at field capacity conditions. Land use and moisture were significant factors influencing the composition of methanogenic and methanotrophic communities. The diversity and evenness of methanogens did not change throughout the experiment. In contrast, methanotrophs exhibited the highest diversity and evenness in pasture soils at 100% FC. Taken together, our results suggest that increased moisture exacerbates soil CH4 emissions and microbial responses driven by land-use change in the Amazon. This is the first report on the microbial CH4 cycle in Amazonian upland soils that combined one-month gas measurements with advanced molecular methods.

Community structure - Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function.

Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. We test these assertions using methane (CH4 )-cycling processes in soil samples collected along a wetland-to-upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high-affinity CH4 oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scale processes. In addition, the nature of those relationships differed, with broad processes limited by abundance while fine-scale processes were associated with diversity and composition. These findings demonstrate the importance of carefully defining the physiological scale of ecosystem function and performing community measurements that represent the range of possible controls on ecosystem processes.

Conversion of Amazon rainforest to agriculture alters community traits of methane-cycling organisms.

Land use change is one of the greatest environmental impacts worldwide, especially to tropical forests. The Amazon rainforest has been subject to particularly high rates of land use change, primarily to cattle pasture. A commonly observed response to cattle pasture establishment in the Amazon is the conversion of soil from a methane sink in rainforest, to a methane source in pasture. However, it is not known how the microorganisms that mediate methane flux are altered by land use change. Here, we use the deepest metagenomic sequencing of Amazonian soil to date to investigate differences in methane-cycling microorganisms and their traits across rainforest and cattle pasture soils. We found that methane-cycling microorganisms responded to land use change, with the strongest responses exhibited by methane-consuming, rather than methane-producing, microorganisms. These responses included a reduction in the relative abundance of methanotrophs and a significant decrease in the abundance of genes encoding particulate methane monooxygenase. We also observed compositional changes to methanotroph and methanogen communities as well as changes to methanotroph life history strategies. Our observations suggest that methane-cycling microorganisms are vulnerable to land use change, and this vulnerability may underlie the response of methane flux to land use change in Amazon soils.

Relative abundance data can misrepresent heritability of the microbiome.

BACKGROUND: Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h2) of the microbiome-the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample. RESULTS: We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates. CONCLUSIONS: We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. Video Abstract.

Conspecific versus heterospecific transmission shapes host specialization of the phyllosphere microbiome.

In disease ecology, pathogen transmission among conspecific versus heterospecific hosts is known to shape pathogen specialization and virulence, but we do not yet know if similar effects occur at the microbiome level. We tested this idea by experimentally passaging leaf-associated microbiomes either within conspecific or across heterospecific plant hosts. Although conspecific transmission results in persistent host-filtering effects and more within-microbiome network connections, heterospecific transmission results in weaker host-filtering effects but higher levels of interconnectivity. When transplanted onto novel plants, heterospecific lines are less differentiated by host species than conspecific lines, suggesting a shift toward microbiome generalism. Finally, conspecific lines from tomato exhibit a competitive advantage on tomato hosts against those passaged on bean or pepper, suggesting microbiome-level host specialization. Overall, we find that transmission mode and previous host history shape microbiome diversity, with repeated conspecific transmission driving microbiome specialization and repeated heterospecific transmission promoting microbiome generalism.

Plant neighborhood shapes diversity and reduces interspecific variation of the phyllosphere microbiome.

Microbial communities associated with plant leaf surfaces (i.e., the phyllosphere) are increasingly recognized for their role in plant health. While accumulating evidence suggests a role for host filtering of its microbiota, far less is known about how community composition is shaped by dispersal, including from neighboring plants. We experimentally manipulated the local plant neighborhood within which tomato, pepper, or bean plants were grown in a 3-month field trial. Focal plants were grown in the presence of con- or hetero-specific neighbors (or no neighbors) in a fully factorial combination. At 30-day intervals, focal plants were harvested and replaced with a new age- and species-matched cohort while allowing neighborhood plants to continue growing. Bacterial community profiling revealed that the strength of host filtering effects (i.e., interspecific differences in composition) decreased over time. In contrast, the strength of neighborhood effects increased over time, suggesting dispersal from neighboring plants becomes more important as neighboring plant biomass increases. We next implemented a cross-inoculation study in the greenhouse using inoculum generated from the field plants to directly test host filtering of microbiomes while controlling for directionality and source of dispersal. This experiment further demonstrated that focal host species, the host from which the microbiome came, and in one case the donor hosts' neighbors, contribute to variation in phyllosphere bacterial composition. Overall, our results suggest that local dispersal is a key factor in phyllosphere assembly, and that demographic factors such as nearby neighbor identity and biomass or age are important determinants of phyllosphere microbiome diversity.

Rainforest-to-pasture conversion stimulates soil methanogenesis across the Brazilian Amazon.

The Amazon rainforest is a biodiversity hotspot and large terrestrial carbon sink threatened by agricultural conversion. Rainforest-to-pasture conversion stimulates the release of methane, a potent greenhouse gas. The biotic methane cycle is driven by microorganisms; therefore, this study focused on active methane-cycling microorganisms and their functions across land-use types. We collected intact soil cores from three land use types (primary rainforest, pasture, and secondary rainforest) of two geographically distinct areas of the Brazilian Amazon (Santarém, Pará and Ariquemes, Rondônia) and performed DNA stable-isotope probing coupled with metagenomics to identify the active methanotrophs and methanogens. At both locations, we observed a significant change in the composition of the isotope-labeled methane-cycling microbial community across land use types, specifically an increase in the abundance and diversity of active methanogens in pastures. We conclude that a significant increase in the abundance and activity of methanogens in pasture soils could drive increased soil methane emissions. Furthermore, we found that secondary rainforests had decreased methanogenic activity similar to primary rainforests, and thus a potential to recover as methane sinks, making it conceivable for forest restoration to offset greenhouse gas emissions in the tropics. These findings are critical for informing land management practices and global tropical rainforest conservation.

Belowground changes to community structure alter methane-cycling dynamics in Amazonia.

Amazonian rainforest is undergoing increasing rates of deforestation, driven primarily by cattle pasture expansion. Forest-to-pasture conversion has been associated with increases in soil methane (CH4) emission. To better understand the drivers of this change, we measured soil CH4 flux, environmental conditions, and belowground microbial community structure across primary forests, cattle pastures, and secondary forests in two Amazonian regions. We show that pasture soils emit high levels of CH4 (mean: 3454.6 ± 9482.3 µg CH4 m-2 d-1), consistent with previous reports, while forest soils on average emit CH4 at modest rates (mean: 9.8 ± 120.5 µg CH4 m-2 d-1), but often act as CH4 sinks. We report that secondary forest soils tend to consume CH4 (mean: -10.2 ± 35.7 µg CH4 m-2 d-1), demonstrating that pasture CH4 emissions can be reversed. We apply a novel computational approach to identify microbial community attributes associated with flux independent of soil chemistry. While this revealed taxa known to produce or consume CH4 directly (i.e. methanogens and methanotrophs, respectively), the vast majority of identified taxa are not known to cycle CH4. Each land use type had a unique subset of taxa associated with CH4 flux, suggesting that land use change alters CH4 cycling through shifts in microbial community composition. Taken together, we show that microbial composition is crucial for understanding the observed CH4 dynamics and that microorganisms provide explanatory power that cannot be captured by environmental variables.

Use of RNA and DNA to Identify Mechanisms of Bacterial Community Homogenization.

Biotic homogenization, i.e., the increase in community similarity through time or space, is a commonly observed response following conversion of native ecosystems to agriculture, but our understanding of the ecological mechanisms underlying this process is limited for bacterial communities. Identifying mechanisms of bacterial community homogenization following rapid environmental change may be complicated by the fact only a minority of taxa is active at any time. Here we used RNA- and DNA-based metabarcoding to distinguish putatively active taxa in the bacterial community from inactive taxa. We asked how soil bacterial communities respond to land use change following a rapid transition from rainforest to agriculture in the Congo Basin using a chronosequence that spans from roughly 1 week following slash-and-burn to an active plantation roughly 1.5 years post-conversion. Our results indicate that the magnitude of community homogenization is larger in the RNA-inferred community than the DNA-inferred perspective. We show that as the soil environment changes, the RNA-inferred community structure tracks environmental variation and loses spatial structure. The DNA-inferred community does not respond to environmental variability to the same degree, and is instead homogenized by a subset of taxa that is shared between forest and conversion sites. Our results suggest that complementing DNA-based surveys with RNA can provide insights into the way bacterial communities respond to environmental change.

Why do microbes exhibit weak biogeographic patterns?

Analysis of patterns in the distribution of taxa can provide important insights into ecological and evolutionary processes. Microbial biogeographic patterns almost always appear to be weaker than those reported for plant and animal taxa. It is as yet unclear why this is the case. Some argue that microbial diversity scales differently over space because microbial taxa are fundamentally different in their abundance, longevity and dispersal abilities. Others have argued that differences in scaling are an artifact of how we assess microbial biogeography, driven, for example, by differences in taxonomic resolution, spatial scale, sampling effort or community activity/dormancy. We tested these alternative explanations by comparing bacterial biogeographic patterns in soil to those of trees found in a forest in Gabon. Altering taxonomic resolution, excluding inactive individuals, or adjusting for differences in spatial scale were insufficient to change the rate of microbial taxonomic turnover. In contrast, we account for the differences in spatial turnover between these groups by equalizing sampling extent. Our results suggest that spatial scaling differences between microbial and plant diversity are likely not due to fundamental differences in biology, and that sampling extent should be taken into account when comparing the biogeographic patterns of microorganisms and larger organisms.

New Biological Insights Into How Deforestation in Amazonia Affects Soil Microbial Communities Using Metagenomics and Metagenome-Assembled Genomes.

Deforestation in the Brazilian Amazon occurs at an alarming rate, which has broad effects on global greenhouse gas emissions, carbon storage, and biogeochemical cycles. In this study, soil metagenomes and metagenome-assembled genomes (MAGs) were analyzed for alterations to microbial community composition, functional groups, and putative physiology as it related to land-use change and tropical soil. A total of 28 MAGs were assembled encompassing 10 phyla, including both dominant and rare biosphere lineages. Amazon Acidobacteria subdivision 3, Melainabacteria, Microgenomates, and Parcubacteria were found exclusively in pasture soil samples, while Candidatus Rokubacteria was predominant in the adjacent rainforest soil. These shifts in relative abundance between land-use types were supported by the different putative physiologies and life strategies employed by the taxa. This research provides unique biological insights into candidate phyla in tropical soil and how deforestation may impact the carbon cycle and affect climate change.

Differential Response of Acidobacteria Subgroups to Forest-to-Pasture Conversion and Their Biogeographic Patterns in the Western Brazilian Amazon.

Members of the phylum Acidobacteria are among the most abundant soil bacteria on Earth, but little is known about their response to environmental changes. We asked how the relative abundance and biogeographic patterning of this phylum and its subgroups responded to forest-to-pasture conversion in soils of the western Brazilian Amazon. Pyrosequencing of 16S rRNA genes was employed to assess the abundance and composition of the Acidobacteria community across 54 soil samples taken using a spatially nested sampling scheme at the landscape level. Numerically, Acidobacteria represented 20% of the total bacterial community in forest soils and 11% in pasture soils. Overall, 15 different Acidobacteria subgroups of the current 26 subgroups were detected, with Acidobacteria subgroups 1, 3, 5, and 6 accounting together for 87% of the total Acidobacteria community in forest soils and 75% in pasture soils. Concomitant with changes in soil chemistry after forest-to-pasture conversion-particularly an increase in properties linked to soil acidity and nutrient availability-we observed an increase in the relative abundances of Acidobacteria subgroups 4, 10, 17, and 18, and a decrease in the relative abundances of other Acidobacteria subgroups in pasture relative to forest soils. The composition of the total Acidobacteria community as well as the most abundant Acidobacteria subgroups (1, 3, 5, and 6) was significantly more similar in composition across space in pasture soils than in forest soils. These results suggest that preponderant responses of Acidobacteria subgroups, especially subgroups 1, 3, 4, 5, and 6, to forest-to-pasture conversion effects in soils could be used to define management-indicators of agricultural practices in the Amazon Basin. These acidobacterial responses are at least in part through alterations on acidity- and nutrient-related properties of the Amazon soils.